A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
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TL;DR: The combination of gedatolisib + cofetuzumab pelidotin was well tolerated and demonstrated promising clinical activity in metastatic triple-negative breast cancer as mentioned in this paper .
Abstract: The PI3K pathway is dysregulated in the majority of triple-negative breast cancers (TNBC), yet single-agent inhibition of PI3K has been ineffective in TNBC. PI3K inhibition leads to an immediate compensatory upregulation of the Wnt pathway. Dual targeting of both pathways is highly synergistic against TNBC models in vitro and in vivo. We initiated a phase I clinical trial combining gedatolisib, a pan-class I isoform PI3K/mTOR inhibitor, and cofetuzumab pelidotin, an antibody-drug conjugate against the cell-surface PTK7 protein (Wnt pathway coreceptor) with an auristatin payload.Participants (pt) had metastatic TNBC or estrogen receptor (ER) low (ER and PgR < 5%, HER2-negative) breast cancer, and had received at least one prior chemotherapy for advanced disease. The primary objective was safety. Secondary endpoints included overall response rate (ORR), clinical benefit at 18 weeks (CB18), progression-free survival (PFS), and correlative analyses.A total of 18 pts were enrolled in three dose cohorts: gedatolisib 110 mg weekly + cofetuzumab pelidotin 1.4 mg/kg every 3 weeks (n = 4), 180 mg + 1.4 mg/kg (n = 3), and 180 mg + 2.8 mg/kg (n = 11). Nausea, anorexia, fatigue, and mucositis were common but rarely reached ≥grade 3 severity. Myelosuppression was uncommon. ORR was 16.7% (3/18). An additional 3 pts had stable disease (of these 2 had stable disease for >18 weeks); CB18 was 27.8%. Median PFS was 2.0 months (95% confidence interval for PFS: 1.2-6.2). Pts with clinical benefit were enriched with genomic alterations in the PI3K and PTK7 pathways.The combination of gedatolisib + cofetuzumab pelidotin was well tolerated and demonstrated promising clinical activity. Further investigation of this drug combination in metastatic TNBC is warranted.
3 citations
TL;DR: In this paper, the authors comprehensively review the widely used methods in the above four categories and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
Abstract: Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
3 citations
TL;DR: A robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.
Abstract: Background Epithelial ovarian cancers are age-associated diseases, usually diagnosed at an advanced stage. lncRNA has been discovered to interplay with N6-methyladenosine (m6A), working in tandem to promote cancer progression and worsening patient outcomes. This study is aimed at investigating the roles and mechanism of m6A-related lncRNA signature on ovarian cancers. Methods We retrieved TCGA and CGGA sequencing data to identify m6A-related lncRNA signature and constructed an m6A score (MS) using the LASSO algorithm. A clinical nomogram was then established to predict the overall survival of patients. Subsequently, GSEA analyses were conducted to obtain pathways involved. Expression of HLA genes, 28 tumor-infiltrating lymphocyte infiltration, and anticancer cycle were analyzed the immunological differences between high-MS and low-MS groups. Finally, immune checkpoint gene expressions and IC50 of chemotherapeutic drugs were calculated, and CMap was run to identify the potential compounds and their corresponding mechanisms. Results We identified 16 m6A-related lncRNAs and constructed an MS model. The high-MS group showed a poor prognosis. A clinical nomogram consists of MS, and age was constructed and predicted the 1-, 3-, and 5-year survival with high accuracy. GSEA analyses presented downregulated antigen processing and presentation pathways. Immunocyte infiltrating analyses demonstrated that high-MS was associated with high infiltration of Treg cells, macrophages, and low Th1/Th2 rate. Also, high expression of immune checkpoint genes NRP1, TNFSF9, and VSIR was observed in the high-MS group. Finally, the high-MS group also predicted low IC50 of vinorelbine and vorinostat. Conclusion This study constructed a robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression. Besides, the m6A lncRNA signature can predict the chemotherapeutic drug response. These will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.
3 citations
TL;DR: In this paper , an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information was developed and validated in three independent RNAseq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively.
Abstract: Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. The objective of this study was to develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information. We analyzed RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples. Logistic least absolute shrinkage and selection operator (Lasso) regression analysis in the training set identified 73 differentially expressed genes (DEG) to form a weighted RSRS that discriminated asthmatics from healthy subjects with area under the curve (AUC) of 0.80 in the testing set after adjustment for age and gender. The 73-gene RSRS was validated in three independent RNA-seq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively. To explore their biological and molecular functions in asthma phenotype, we examined the 73 genes by enrichment pathway analysis and found that these genes were significantly (p < 0.0001) enriched for DNA replication, recombination, and repair, cell-to-cell signaling and interaction, and eumelanin biosynthesis and developmental disorder. Further in-silico analyses of the 73 genes using Connectivity map shows that drugs (mepacrine, dactolisib) and genetic perturbagens (PAK1, GSR, RBM15 and TNFRSF12A) were identified and could potentially be repurposed for treating asthma. These findings show the promise for RNA-seq risk scores to stratify and predict disease risk.
3 citations
TL;DR: In this paper, the authors report that prenatal witnessing the defeat process of the mated partner induces anxiety-like behaviors in F1 male, but not female offspring, indicating a sex-specific intergenerational effects.
3 citations
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
10,968 citations
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome
8,326 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations